COMP575

Computational Intelligence

Aims

Understand the basic structures and the learning mechanisms underlying neural networks within the field of artificial intelligence and examine how synaptic adaptation can facilitate learning and how input to output mapping can be performed by neural networks.

Obtain an overview of linear, nonlinear, separable and non separable classification as well as supervised and unsupervised mapping.

Understand the benefit of adopting naturally inspired techniques to implement optimisation of complex systems and acquire the fundamental knowledge in various evolutionary techniques.

Become familiar with the basic concepts of systems optimisation and its role in natural and biological systems and entities.

An Introduction to Genetic Algorithms for Scientists and Engineers , DA Coley.

A Genetic Algorithm Tutorial, Darrell Whitley.​

Learning Outcomes

​Learning the advantages and main characteristics of neural networks in relation to traditional methodologies. Also, familiarity with different neural networks structures and their learning mechanisms.
​​​​Appreciation of the advantages of evolutionary-related approaches for optimisation problems and their advantages compared to traditional methodologies. Also, understanding the different techniques of evolutionary optimisation for discrete and continuous configurations.​Understanding of the needs for genetic encoding and modelling for solving optimisation problems and familiarisation with the evolutionary operators and their performance.​​​​​​Understanding of the neural network learning processes and their most popular types, as well as appreciation of how neural networks can be applied to artificial intelligence problems.

Learning Strategy

Slide based presentations and blackboard for both lectures and tutorials

This module will be delivered through a combination of formal lectures and tutorials. Fully comprehensive notes have been designed to cover the major and most commonly used types of neural networks (part I) and also the most popular types of evolutionary optimisation (part II). Extra material will be offered to the students optionally to enhance their learning experience.